We consider the problem of determining feasible systems from a finite set of simulated alternatives with respect to probability constraints, where the observations from stochastic simulations are Bernoulli distributed. Most statistically valid procedures for feasibility determination focus on constraints on the means of normally distributed observations. Although these procedures can be adapted to Bernoulli-distributed data by treating batch means as basic observations, achieving approximate normality often requires a large batch size, potentially leading to the unnecessary waste of observations in reaching a decision. This paper proposes a procedure that utilizes the Bernoulli-distributed observations directly to determine feasibility. In addition, we incorporate subjective constraints, allowing for multiple thresholds for each constraint. We demonstrate that our proposed procedure is statistically valid and that it outperforms an existing feasibility determination procedure for subjective constraints originally developed for normally distributed observations. Furthermore, we propose two heuristic feasibility check approaches for thresholds that are sequentially added by decision makers, allowing thresholds to be tightened when many systems are feasible or relaxed when no feasible system exists. We show by experiments that the proposed procedures can efficiently provide feasibility decisions to systems with respect to all thresholds considered.

翻译：我们考虑从有限个模拟备选方案中，基于概率约束判定可行系统的问题，其中随机模拟的观测值服从伯努利分布。现有的大多数统计有效的可行性判定方法均假设观测值服从正态分布，并针对其均值约束设计。尽管这些方法可通过将批均值作为基本观测值来适配伯努利分布数据，但为达到近似正态性往往需要较大的批尺寸，这可能导致决策过程中观测值的非必要浪费。本文提出一种直接利用伯努利分布观测值进行可行性判定的方法。此外，我们引入主观约束，允许为每个约束设定多个阈值。我们证明所提方法具有统计有效性，且在面向原为正态分布观测值设计的主观约束可行性判定方法中，本方法表现更优。进一步，针对决策者逐步添加的阈值，我们提出两种启发式可行性校验策略：当多数系统可行时可收紧阈值，当无可行系统时可放宽阈值。实验表明，所提方法能针对所有待考量阈值，高效地为系统提供可行性判定结果。